A rustle in the bushes or a movement in the grass that cannot be audibly perceived provides visual cues based upon the motion of the leaves or movement of the grass. Image representations, such as shapes, size, color, position, and others, effectively prime the pre-attentive process of the human visual system.
Visual items often consist of regions that differ in color, luminance, size, and shape. The human visual system is adept at binding together these various regions to perceive the whole object, while simultaneously separating the regions from those that belong to other objects or to the background. The human visual system parses the regions and recognizes whole objects. Motion cues play a significant role in the process of recognizing objects. Objects that are substantially similar to other objects within a field of vision are more difficult to discern. Motion cues enable integration of regions within an object as well as the ability to separate the object from the background. Additionally, motion cues facilitate development of object representations, which permit recognition in static images. Motion information plays a fundamental role in organizing the visual experience and in assisting a viewer in both integrating regions within an object and in separating one object from another or from a background.
Standard data displays do not fully utilize the exceptional ability of the human visual processing system to respond to motion cues. Moreover, standard techniques often display patterns with 1) spatial interruptions; or 2) spatially separation in the display. Long, stretched out representations of data are not ideal for the visual identification or separation of multiple, interwoven patterns. Even in the case of a single pattern, small systematic deviations from a trial model for the pattern become hard to judge from an extended, stretched-out representation. Paradoxically, a standard x-y plot can exhibit these problems even when the underlying data contains elegant symmetry. These representation problems may be indicative that the visualization technique itself may be unnecessarily encumbering the processes of data exploration, hypothesis generation and analysis. Addressing this type of visualization bottleneck ameliorates a viewer's ability to explore and present global trends in data, and to avoid limiting the presentation to a small, localized window when comparing experimental data with modeled data behaviors.
Dynamic graphics may be incorporated into data analysis, including dynamic regression analysis. However, for data involving strongly interwoven patterns, the residuals with respect to a model for the pattern become difficult to calculate. To calculate these residuals, numerical algorithms have been used. However, by introducing numerical algorithms as an intermediate step in the analysis, there are risks for error. For example, when using numerical algorithms, one might make a priori assumptions about trends in the data, or introduce errors in performing peak fitting. A direct, rapid way to produce the dynamic regression plot without calculating the residuals is needed.
The extent to which an imperfect visual display is relied upon in the process of in setting up the numerical algorithm creates a circular problem, and is also frequently overlooked. An advanced repertoire of numerical algorithms exists for analyzing data containing patterns of peaks. For example, such algorithms may be applied to audio and speech analysis. These algorithms often operate by reducing the data to a single number or perhaps a few numbers (such as a set of fundamental frequencies), but in doing so hide complex relationships in the data. In the case where knowledge of the trends in the data that contribute to the final output is important to the user, reduction of the data to a single number overprojects the data. As a result, when working with the numerical algorithms, such as in debugging, in developing, and in other environments, one typically uses an x-y plot of the raw data in an attempt to monitor the trends, which in turn produces the bottlenecks described above, namely spatial separation and spatial interruption of patterns. Some numerical algorithms produce results that are directly shown in x-y plot form, such as cepstrum, autocorrelation, and the like, and these processes exhibit these same problems. Data analyzed via the short time Fourier transform are presented in 2-D. However, patterns are spatially separated along the y-axis.
Mappings may be used to transform visual proximity. In acoustics and psychoacoustics research, mappings of pitch onto cyclic curves are used to increase the proximity of members of a given pitch class. A variety of different mappings have been developed, from the Pythagorean spiral of fifths to the helical representation of pitch defined by Drobisch as well as those discussed by Ruckmick, and in the study and family of mappings constructed by R. N. Shepard, which includes a three dimensional toroidal map. Pikler presented a historical perspective of pitch-related computations utilizing spirals and reviewed the work of Ptolemy and others. Pikler also reported new developments and experimented with the imaginary domain. More recently, Chew created the “Spiral Array”, and has explored pitch spelling applications. However, all of these techniques focus on musical pitch and tend to spatially disperse patterns in spectral harmonics, as they are optimized for a different set of applications. Further, these techniques do not include motion to organize the visual experience and to assist a viewer in both integrating regions within an object and in separating one object from another or from a background.
Another manner of increasing the proximity of stretched-out serial data is raster scanning. Raster scanning is an example of transforming serial data so that proximity of stretched-out patterns is increased. Early work performed by Lashinsky produced raster plots of spectra using an experimental setup that integrated a spectrum analyzer with an oscilloscope, using the latter to produce the final display. Lashinsky's technique seeks periodicity in spectral data and was used to display patterns of harmonics in spectra. However, this scheme not only does not incorporate motion, but removes it through its triggering method. For sufficiently tangled spectra, static displays can leave patterns hidden even when they remap the data to increase proximity.
When a frequency spectrum or block of data is presented in serial form, real effects may exist within the spectrum or block of data that are difficult for the eye to perceive. Graphical transformations of the data may better illustrate these effects and enhance the process of data exploration and algorithm development. Long, stretched-out serial visual patterns are easier to recognize and compare as compact objects.
Motion cues facilitate development of object representations, but efforts to date have not successfully incorporated motion with compact objects to provide recognition.
Efforts to date to improve the ability to recognize and characterize patterns within data presentations have been largely unsuccessful in providing graphical techniques that provide visual recognition of systematic frequency effects and data patterns. Efforts aimed at improving the ability to accurately identify harmonics, spectral features, and other data patterns have not provided satisfactory results. What is needed is a system and a method for accurately identifying data patterns using compact data and motion enhancement to provide visual recognition of frequency effects and data patterns.